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NEW QUESTION: 1
150を超える機能を含むデータセットがあります。 データセットを使用して、サポートベクターマシン（SVM）バイナリ分類器をトレーニングします。
Azure Machine Learning StudioのPermutation Feature Importanceモジュールを使用して、データセットの一連の機能重要度スコアを計算する必要があります。
どの順序でアクションを実行する必要がありますか？ 回答するには、すべてのアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation

Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
Step 2: Add a dataset to the experiment
Step 3: Add a Split Data module to create training and test dataset.
To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.
Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.
Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-mac
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importan

NEW QUESTION: 2

A. Option D
B. Option A
C. Option F
D. Option B
E. Option C
F. Option E
Answer: A,D,F
Explanation:
Explanation
B (not A): The PDB must be closed before unplugging it.
D: An unplugged PDB contains data dictionary tables, and some of the columns in these encode information in an endianness-sensitive way. There is no supported way to handle the conversion of such columns automatically. This means, quite simply, that an unplugged PDB cannot be moved across an endianness difference.
E (not F): To exploit the new unplug/plug paradigm for patching the Oracle version most effectively, the source and destination CDBs should share a filesystem so that the PDB's datafiles can remain in place.
References:

NEW QUESTION: 3
Pardotトラッキングコードはどこにありますか。サイト訪問者のCookieの設定を開始するために挿入することができますか。
A. マーケティング|トラッキングコード
B. 管理| トラッキングコードを見る
C. マーケティング キャンペーン ウェブサイトの追跡| トラッキングコードを見る
D. レポート キャンペーン トラッキングコードを見る
Answer: C